Bayesian Biosurveillance of Disease Outbreaks

TL;DR

This paper explores the use of scalable causal Bayesian networks for real-time biosurveillance of non-contagious diseases, demonstrating their potential for effective outbreak detection in large populations.

Contribution

It introduces techniques to manage model complexity and inference in large-scale Bayesian networks, enabling real-time disease outbreak surveillance.

Findings

Bayesian networks can be scaled to millions of nodes for biosurveillance

Techniques for managing parameters improve inference efficiency

Proof-of-concept shows effectiveness in outbreak detection

Abstract

Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.